Differential modulation of FXR activity by chlorophacinone and ivermectin analogs.

Chemicals that alter normal function of farnesoid X receptor (FXR) have been shown to affect the homeostasis of bile acids, glucose, and lipids. Several structural classes of environmental chemicals and drugs that modulated FXR transactivation were previously identified by quantitative high-throughput screening (qHTS) of the Tox21 10K chemical collection. In the present study, we validated the FXR antagonist activity of selected structural classes, including avermectin anthelmintics, dihydropyridine calcium channel blockers, 1,3-indandione rodenticides, and pyrethroid pesticides, using in vitro assay and quantitative structural-activity relationship (QSAR) analysis approaches. (Z)-Guggulsterone, chlorophacinone, ivermectin, and their analogs were profiled for their ability to alter CDCA-mediated FXR binding using a panel of 154 coregulator motifs and to induce or inhibit transactivation and coactivator recruitment activities of constitutive androstane receptor (CAR), liver X receptor alpha (LXRα), or pregnane X receptor (PXR). Our results showed that chlorophacinone and ivermectin had distinct modes of action (MOA) in modulating FXR-coregulator interactions and compound selectivity against the four aforementioned functionally-relevant nuclear receptors. These findings collectively provide mechanistic insights regarding compound activities against FXR and possible explanations for in vivo toxicological observations of chlorophacinone, ivermectin, and their analogs.

Removal of Cu(II) from aqueous solution using synthetic poly(catechol-diethylenetriamine-p-phenylenediamine) particles.

A novel poly(catechol-diethylenetriamine-pphenylenediamine)(PCEA) adsorbent was synthesized in methanol, with chelating groups supplied by catechol and diethylenetriamine, which showed a strong removal performance and efficient adsorption toward Cu(II) ions in aqueous solution. The adsorbent was characterized by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA). Besides, factors such as adsorbent dosage, pH, initial ionic and metal concentrations, contact time, and temperature on the adsorption of Cu(II) were studied. The data revealed that the adsorption followed a pseudo-second order kinetic model and the adsorption rate was influenced by the intra-particle diffusion. Furthermore, the adsorption process followed the Langmuir isotherm model, and the maximum adsorption capacity (Qm) was 44.2mg/g at 298K in simulated wastewater. The value of ΔG (kJ/mol) and ΔH (kJ/mol) also demonstrated that the adsorption process was spontaneous and endothermic. Studies revealed that PCEA particles were powerful and stable for the removal of Cu(II) in water, and it could be directly applied to the Cu(II)-contaminated water.

An unusual metallic oxygen cluster consisting of a {AlMo12O40(MoO2)}.

A novel aluminum-containing, bimolybdenum-capped Keggin-type polyoxomolybate cluster of the {AlMo12O40(MoO2)2} anion has been synthesized and characterized. This is the first experimentally determined Keggin-type {AlMo12O40} polyoxoanion. The polyoxometalate crystal exhibits high selectivity toward the heterogeneous catalytic oxidation of cyclohexanol to cyclohexanone.

Integrated Approach for Testing and Assessment for Developmental Neurotoxicity (DNT) to Prioritize Aromatic Organophosphorus Flame Retardants.

Organophosphorus flame retardants (OPFRs) are abundant and persistent in the environment but have limited toxicity information. Their similarity in structure to organophosphate pesticides presents great concern for developmental neurotoxicity (DNT). However, current in vivo testing is not suitable to provide DNT information on the amount of OPFRs that lack data. Over the past decade, an in vitro battery was developed to enhance DNT assessment, consisting of assays that evaluate cellular processes in neurodevelopment and function. In this study, behavioral data of small model organisms were also included. To assess if these assays provide sufficient mechanistic coverage to prioritize chemicals for further testing and/or identify hazards, an integrated approach to testing and assessment (IATA) was developed with additional information from the Integrated Chemical Environment (ICE) and the literature. Human biomonitoring and exposure data were identified and physiologically-based toxicokinetic models were applied to relate in vitro toxicity data to human exposure based on maximum plasma concentration. Eight OPFRs were evaluated, including aromatic OPFRs (triphenyl phosphate (TPHP), isopropylated phenyl phosphate (IPP), 2-ethylhexyl diphenyl phosphate (EHDP), tricresyl phosphate (TMPP), isodecyl diphenyl phosphate (IDDP), tert-butylphenyl diphenyl phosphate (BPDP)) and halogenated FRs ((Tris(1,3-dichloro-2-propyl) phosphate (TDCIPP), tris(2-chloroethyl) phosphate (TCEP)). Two representative brominated flame retardants (BFRs) (2,2'4,4'-tetrabromodiphenyl ether (BDE-47) and 3,3',5,5'-tetrabromobisphenol A (TBBPA)) with known DNT potential were selected for toxicity benchmarking. Data from the DNT battery indicate that the aromatic OPFRs have activity at similar concentrations as the BFRs and should therefore be evaluated further. However, these assays provide limited information on the mechanism of the compounds. By integrating information from ICE and the literature, endocrine disruption was identified as a potential mechanism. This IATA case study indicates that human exposure to some OPFRs could lead to a plasma concentration similar to those exerting in vitro activities, indicating potential concern for human health.

A Pragmatic Framework for the Application of New Approach Methodologies in One Health Toxicological Risk Assessment.

Globally, industries and regulatory authorities are faced with an urgent need to assess the potential adverse effects of chemicals more efficiently by embracing new approach methodologies (NAMs). NAMs include cell and tissue methods (in vitro), structure-based/toxicokinetic models (in silico), methods that assess toxicant interactions with biological macromolecules (in chemico), and alternative models. Increasing knowledge on chemical toxicokinetics (what the body does with chemicals) and toxicodynamics (what the chemicals do with the body) obtained from in silico and in vitro systems continues to provide opportunities for modernizing chemical risk assessments. However, directly leveraging in vitro and in silico data for derivation of human health-based reference values has not received regulatory acceptance due to uncertainties in extrapolating NAM results to human populations, including metabolism, complex biological pathways, multiple exposures, interindividual susceptibility and vulnerable populations. The objective of this article is to provide a standardized pragmatic framework that applies integrated approaches with a focus on quantitative in vitro to in vivo extrapolation (QIVIVE) to extrapolate in vitro cellular exposures to human equivalent doses from which human reference values can be derived. The proposed framework intends to systematically account for the complexities in extrapolation and data interpretation to support sound human health safety decisions in diverse industrial sectors (food systems, cosmetics, industrial chemicals, pharmaceuticals etc.). Case studies of chemical entities, using new and existing data, are presented to demonstrate the utility of the proposed framework while highlighting potential sources of human population bias and uncertainty, and the importance of Good Method and Reporting Practices.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data.

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R2 of 0.52-0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R2 for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

Surface-redox sodium-ion storage in anatase titanium oxide.

Sodium-ion storage technologies are promising candidates for large-scale grid systems due to the abundance and low cost of sodium. However, compared to well-understood lithium-ion storage mechanisms, sodium-ion storage remains relatively unexplored. Herein, we systematically determine the sodium-ion storage properties of anatase titanium dioxide (TiO2(A)). During the initial sodiation process, a thin surface layer (~3 to 5 nm) of crystalline TiO2(A) becomes amorphous but still undergoes Ti4+/Ti3+ redox reactions. A model explaining the role of the amorphous layer and the dependence of the specific capacity on the size of TiO2(A) nanoparticles is proposed. Amorphous nanoparticles of ~10 nm seem to be optimum in terms of achieving high specific capacity, on the order of 200 mAh g-1, at high charge/discharge rates. Kinetic studies of TiO2(A) nanoparticles indicate that sodium-ion storage is due to a surface-redox mechanism that is not dependent on nanoparticle size in contrast to the lithiation of TiO2(A) which is a diffusion-limited intercalation process. The surface-redox properties of TiO2(A) result in excellent rate capability, cycling stability and low overpotentials. Moreover, tailoring the surface-redox mechanism enables thick electrodes of TiO2(A) to retain high rate properties, and represents a promising direction for high-power sodium-ion storage.

Interface Modulation of Metal Sulfide Anodes for Long-Cycle-Life Sodium-Ion Batteries.

Although studies of transition metal sulfides (TMS) as anode materials for sodium-ion batteries are extensively reported, the short cycle life is still a thorny problem that impedes their practical application. In this work, a new capacity fading mechanism of the TMS electrodes is demonstrated; that is, the parasitic reaction between electrolyte anions (i.e., ClO4 - ) and metal sulfides yields non-conductive and unstable solid-electrolyte interphase (SEI) and meanwhile, corrosively turns metal sulfides into less-active oxides. This knowledge guides the development of an electrochemical strategy to manipulate the anion decomposition and construct a stable interface that prevents extensive parasitic reactions. It is shown that introducing sodium nitrate to the electrolyte radically changes the Na+ solvation structure by populating nitrate ions in the first solvation sheath, generating a stable and conductive SEI layer containing both Na3 N and NaF. The optimized interface enables an iron sulfide anode to stably cycle for over 2000 cycles with negligible capacity loss, and a similar enhancement in cycle performance is demonstrated on a number of other metal sulfides. This work discloses metal sulfides' cycling failure mechanism from a unique perspective and highlights the critical importance of manipulating the interface chemistry in sodium-ion batteries.

A comprehensive evaluation of spontaneous pelvic organ prolapse in rhesus macaques as an ideal model for the study of human pelvic organ prolapse.

Pelvic organ prolapse (POP) seriously affects a woman's quality of life, and the treatment complications are severe. Although new surgical treatments are being developed, the host tissue responses and safety need to be evaluated in preclinical trials. However, there is a lack of suitable animal models, as most quadrupeds exhibit different structural and pathological changes. In this study, 72 elderly rhesus macaques (Macaca mulatta) were physically examined, and the incidence of spontaneous POP was similar to that in humans. The vaginal wall from five control monkeys and four monkeys with POP were selected for further analysis. Verhoeff-van Gieson staining showed that elastin content decreased significantly in monkeys with POP compared with control samples. Immunohistological staining revealed that the smooth muscle bundles in monkey POP appeared disorganized, and the number of large muscle bundles decreased significantly. The collagen I/III ratio in monkey POP also significantly decreased, as revealed by Sirius Red staining. These histological and biochemical changes in monkeys with POP were similar to those in humans with POP. Moreover, we generated a single-cell transcriptomic atlas of the prolapsed monkey vagina. Cross-species analysis between humans and monkeys revealed a comparable cellular composition. Notably, a differential gene expression analysis determined that dysregulation of the extracellular matrix and an immune disorder were the conserved molecular mechanisms. The interplay between fibroblasts and macrophages contributed to human and monkey POP. Overall, this study represents a comprehensive evaluation of spontaneous POP in rhesus macaques and demonstrates that monkeys are a suitable animal model for POP research.

Evaluation of the herbicide glyphosate, (aminomethyl)phosphonic acid, and glyphosate-based formulations for genotoxic activity using in vitro assays.

Glyphosate, the most heavily used herbicide world-wide, is applied to plants in complex formulations that promote absorption. The National Toxicology Program reported in 1992 that glyphosate, administered to rats and mice at doses up to 50,000 ppm in feed for 13 weeks, showed little evidence of toxicity, and no induction of micronuclei was observed in the mice in this study. Subsequently, mechanistic studies of glyphosate and glyphosate-based formulations (GBFs) that have focused on DNA damage and oxidative stress suggest that glyphosate may have genotoxic potential. However, few of these studies directly compared glyphosate to GBFs, or effects among GBFs. To address these data gaps, we tested glyphosate, glyphosate isopropylamine (IPA), and (aminomethyl)phosphonic acid (AMPA, a microbial metabolite of glyphosate), 9 high-use agricultural GBFs, 4 residential-use GBFs, and additional herbicides (metolachlor, mesotrione, and diquat dibromide) present in some of the GBFs in bacterial mutagenicity tests, and in human TK6 cells using a micronucleus assay and a multiplexed DNA damage assay. Our results showed no genotoxicity or notable cytotoxicity for glyphosate or AMPA at concentrations up to 10 mM, while all GBFs and herbicides other than glyphosate were cytotoxic, and some showed genotoxic activity. An in vitro to in vivo extrapolation of results for glyphosate suggests that it is of low toxicological concern for humans. In conclusion, these results demonstrate a lack of genotoxicity for glyphosate, consistent with observations in the NTP in vivo study, and suggest that toxicity associated with GBFs may be related to other components of these formulations.

Application of an Accessible Interface for Pharmacokinetic Modeling and In Vitro to In Vivo Extrapolation.

Regulatory toxicology testing has traditionally relied on in vivo methods to inform decision-making. However, scientific, practical, and ethical considerations have led to an increased interest in the use of in vitro and in silico methods to fill data gaps. While in vitro experiments have the advantage of rapid application across large chemical sets, interpretation of data coming from these non-animal methods can be challenging due to the mechanistic nature of many assays. In vitro to in vivo extrapolation (IVIVE) has emerged as a computational tool to help facilitate this task. Specifically, IVIVE uses physiologically based pharmacokinetic (PBPK) models to estimate tissue-level chemical concentrations based on various dosing parameters. This approach is used to estimate the administered dose needed to achieve in vitro bioactivity concentrations within the body. IVIVE results can be useful to inform on metrics such as margin of exposure or to prioritize potential chemicals of concern, but the PBPK models used in this approach have extensive data requirements. Thus, access to input parameters, as well as the technical requirements of applying and interpreting models, has limited the use of IVIVE as a routine part of in vitro testing. As interest in using non-animal methods for regulatory and research contexts continues to grow, our perspective is that access to computational support tools for PBPK modeling and IVIVE will be essential for facilitating broader application and acceptance of these techniques, as well as for encouraging the most scientifically sound interpretation of in vitro results. We highlight recent developments in two open-access computational support tools for PBPK modeling and IVIVE accessible via the Integrated Chemical Environment (https://ice.ntp.niehs.nih.gov/), demonstrate the types of insights these tools can provide, and discuss how these analyses may inform in vitro-based decision making.

An Ultrahigh-Power Mesocarbon Microbeads|Na+ -Diglyme|Na3 V2 (PO4 )3 Sodium-Ion Battery.

Sodium-ion batteries (SIBs) show practical applications in large-scale energy storage systems. But, their power density is limited by the sluggish Na+ diffusion into the cathode and anode materials. Herein, the authors demonstrate a prototype of ultrahigh power SIB, consisting of the high-rate Na3 V2 (PO4 )3 (NVP) cathode, graphite-type mesocarbon microbeads (MCMB) anode, and Na+ -diglyme electrolyte. It is found that the overpotential of the NVP cathode obeys the Ohmic rule. Thus, the as-synthesized NVP@C@carbon nanotubes (CNTs) cathode with the high conductive CNTs networks displays high electronic conductivity, reducing the overpotential and charge transfer resistances and leading to the remarkable rate capability over 1000C. For the MCMB anode, the initial [Na-diglyme]+ co-intercalation step is pseudocapacitive dominated, and then the expanded graphite's layers ensure the subsequent fast ions diffusion. The rapid (de)intercalation kinetics in between the cathode and anode are well-matched. Thus, the assembled MCMB|1 m NaPF6 in diglyme|NVP@C@CNTs full-cell SIB delivers the energy density of 88 Wh kg-1 at the high power density of ≈10 kW kg-1 . Even at the ultrahigh power density of 23 kW kg-1 , an energy density of 58 Wh kg-1 is obtained. The encouraging results of the full cell will promote the development of high-power SIB for large-scale applications in the future.

Quantitative in vitro to in vivo extrapolation for developmental toxicity potency of valproic acid analogues.

BACKGROUND: The developmental toxicity potential (dTP) concentration from the devTOX quickPredict (devTOXqP ) assay, a metabolomics-based human induced pluripotent stem cell assay, predicts a chemical's developmental toxicity potency. Here, in vitro to in vivo extrapolation (IVIVE) approaches were applied to address whether the devTOXqP assay could quantitatively predict in vivo developmental toxicity lowest effect levels (LELs) for the prototypical teratogen valproic acid (VPA) and a group of structural analogues. METHODS: VPA and a series of structural analogues were tested with the devTOXqP assay to determine dTP concentration and we estimated the equivalent administered doses (EADs) that would lead to plasma concentrations equivalent to the in vitro dTP concentrations. The EADs were compared to the LELs in rat developmental toxicity studies, human clinical doses, and EADs reported using other in vitro assays. To evaluate the impact of different pharmacokinetic (PK) models on IVIVE outcomes, we compared EADs predicted using various open-source and commercially available PK and physiologically based PK (PBPK) models. To evaluate the effect of in vitro kinetics, an equilibrium distribution model was applied to translate dTP concentrations to free medium concentrations before subsequent IVIVE analyses. RESULTS: The EAD estimates for the VPA analogues based on different PK/PBPK models were quantitatively similar to in vivo data from both rats and humans, where available, and the derived rank order of the chemicals was consistent with observed in vivo developmental toxicity. Different models were identified that provided accurate predictions for rat prenatal LELs and conservative estimates of human safe exposure. The impact of in vitro kinetics on EAD estimates is chemical-dependent. EADs from this study were within range of predicted doses from other in vitro and model organism data. CONCLUSIONS: This study highlights the importance of pharmacokinetic considerations when using in vitro assays and demonstrates the utility of the devTOXqP human stem cell-based platform to quantitatively assess a chemical's developmental toxicity potency.

U.S. Federal Agency interests and key considerations for new approach methodologies for nanomaterials.

Engineered nanomaterials (ENMs) come in a wide array of shapes, sizes, surface coatings, and compositions, and often possess novel or enhanced properties compared to larger sized particles of the same elemental composition. To ensure the safe commercialization of products containing ENMs, it is important to thoroughly understand their potential risks. Given that ENMs can be created in an almost infinite number of variations, it is not feasible to conduct in vivo testing on each type of ENM. Instead, new approach methodologies (NAMs) such as in vitro or in chemico test methods may be needed, given their capacity for higher throughput testing, lower cost, and ability to provide information on toxicological mechanisms. However, the different behaviors of ENMs compared to dissolved chemicals may challenge safety testing of ENMs using NAMs. In this study, member agencies within the Interagency Coordinating Committee on the Validation of Alternative Methods were queried about what types of ENMs are of agency interest and whether there is agency-specific guidance for ENM toxicity testing. To support the ability of NAMs to provide robust results in ENM testing, two key issues in the usage of NAMs, namely dosimetry and interference/bias controls, are thoroughly discussed.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making.

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

Exploring in vitro to in vivo extrapolation for exposure and health impacts of e-cigarette flavor mixtures.

In vitro to in vivo extrapolation (IVIVE) leverages in vitro biological activities to predict corresponding in vivo exposures, therefore potentially reducing the need for animal safety testing that are traditionally performed to support the hazard and risk assessment. Interpretation of IVIVE predictions are affected by various factors including the model type, exposure route and kinetic assumptions for the test article, and choice of in vitro assay(s) that are relevant to clinical outcomes. Exposure scenarios are further complicated for mixtures where the in vitro activity may stem from one or more components in the mixture. In this study, we used electronic cigarette (EC) aerosols, a complex mixture, to explore impacts of these factors on the use of IVIVE in hazard identification, using open-source pharmacokinetic models of varying complexity and publicly available data. Results suggest in vitro assay selection has a greater impact on exposure estimates than modeling approaches. Using cytotoxicity assays, high exposure estimates (>1000 EC cartridges (pods) or > 700 mL EC liquid per day) would be needed to obtain the in vivo plasma levels that are corresponding to in vitro assay data, suggesting acute toxicity would be unlikely in typical usage scenarios. When mechanistic (Tox21) assays were used, the exposure estimates were much lower for the low end, but the range of exposure estimate became wider across modeling approaches. These proof-of-concept results highlight challenges and complexities in IVIVE for mixtures.

Integrating endocrine-related health effects into comparative human toxicity characterization.

Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the environment, endocrine-related human health effects remain largely outside of comparative human toxicity characterization frameworks as applied for example in life cycle impact assessments. In this paper, we propose a new methodological framework to consistently integrate endocrine-related health effects into comparative human toxicity characterization. We present two quantitative and operational approaches for extrapolating towards a common point of departure from both in vivo and dosimetry-adjusted in vitro endocrine-related effect data and deriving effect factors as well as corresponding characterization factors for endocrine-active/endocrine-disrupting chemicals. Following the proposed approaches, we calculated effect factors for 323 chemicals, reflecting their endocrine potency, and related characterization factors for 157 chemicals, expressing their relative endocrine-related human toxicity potential. Developed effect and characterization factors are ready for use in the context of chemical prioritization and substitution as well as life cycle impact assessment and other comparative assessment frameworks. Endocrine-related effect factors were found comparable to existing effect factors for cancer and non-cancer effects, indicating that (1) the chemicals' endocrine potency is not necessarily higher or lower than other effect potencies and (2) using dosimetry-adjusted effect data to derive effect factors does not consistently overestimate the effect of potential endocrine disruptors. Calculated characterization factors span over 8-11 orders of magnitude for different substances and emission compartments and are dominated by the range in endocrine potencies.

Evaluation of Inhalation Exposures and Potential Health Impacts of Ingredient Mixtures Using in vitro to in vivo Extrapolation.

In vitro methods offer opportunities to provide mechanistic insight into bioactivity as well as human-relevant toxicological assessments compared to animal testing. One of the challenges for this task is putting in vitro bioactivity data in an in vivo exposure context, for which in vitro to in vivo extrapolation (IVIVE) translates in vitro bioactivity to clinically relevant exposure metrics using reverse dosimetry. This study applies an IVIVE approach to the toxicity assessment of ingredients and their mixtures in e-cigarette (EC) aerosols as a case study. Reported in vitro cytotoxicity data of EC aerosols, as well as in vitro high-throughput screening (HTS) data for individual ingredients in EC liquids (e-liquids) are used. Open-source physiologically based pharmacokinetic (PBPK) models are used to calculate the plasma concentrations of individual ingredients, followed by reverse dosimetry to estimate the human equivalent administered doses (EADs) needed to obtain these plasma concentrations for the total e-liquids. Three approaches (single actor approach, additive effect approach, and outcome-oriented ingredient integration approach) are used to predict EADs of e-liquids considering differential contributions to the bioactivity from the ingredients (humectant carriers [propylene glycol and glycerol], flavors, benzoic acid, and nicotine). The results identified critical factors for the EAD estimation, including the ingredients of the mixture considered to be bioactive, in vitro assay selection, and the data integration approach for mixtures. Further, we introduced the outcome-oriented ingredient integration approach to consider e-liquid ingredients that may lead to a common toxicity outcome (e.g., cytotoxicity), facilitating a quantitative evaluation of in vitro toxicity data in support of human risk assessment.

Validation and characterization of DNA microarray gene expression data distribution and associated moments.

BACKGROUND: The data from DNA microarrays are increasingly being used in order to understand effects of different conditions, exposures or diseases on the modulation of the expression of various genes in a biological system. This knowledge is then further used in order to generate molecular mechanistic hypotheses for an organism when it is exposed to different conditions. Several different methods have been proposed to analyze these data under different distributional assumptions on gene expression. However, the empirical validation of these assumptions is lacking. RESULTS: Best fit hypotheses tests, moment-ratio diagrams and relationships between the different moments of the distribution of the gene expression was used to characterize the observed distributions. The data are obtained from the publicly available gene expression database, Gene Expression Omnibus (GEO) to characterize the empirical distributions of gene expressions obtained under varying experimental situations each of which providing relatively large number of samples for hypothesis testing. All data were obtained from either of two microarray platforms--the commercial Affymetrix mouse 430.2 platform and a non-commercial Rosetta/Merck one. The data from each platform were preprocessed in the same manner. CONCLUSIONS: The null hypotheses for goodness of fit for all considered univariate theoretical probability distributions (including the Normal distribution) are rejected for more than 50% of probe sets on the Affymetrix microarray platform at a 95% confidence level, suggesting that under the tested conditions a priori assumption of any of these distributions across all probe sets is not valid. The pattern of null hypotheses rejection was different for the data from Rosetta/Merck platform with only around 20% of the probe sets failing the logistic distribution goodness-of-fit test. We find that there are statistically significant (at 95% confidence level based on the F-test for the fitted linear model) relationships between the mean and the logarithm of the coefficient of variation of the distributions of the logarithm of gene expressions. An additional novel statistically significant quadratic relationship between the skewness and kurtosis is identified. Data from both microarray platforms fail to identify with any one of the chosen theoretical probability distributions from an analysis of the l-moment ratio diagram.

Ligand-independent regulation of transforming growth factor beta1 expression and cell cycle progression by the aryl hydrocarbon receptor.

The aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor that mediates the toxic effects of its xenobiotic ligands and acts as an environmental checkpoint during the cell cycle. We expressed stably integrated, Tet-Off-regulated AHR variants in fibroblasts from AHR-null mice to further investigate the AHR role in cell cycle regulation. Ahr+/+ fibroblasts proliferated significantly faster than Ahr-/- fibroblasts did, and exposure to a prototypical AHR ligand or deletion of the ligand-binding domain did not change their proliferation rates, indicating that the AHR function in cell cycle was ligand independent. Growth-promoting genes, such as cyclin and cyclin-dependent kinase genes, were significantly down-regulated in Ahr-/- cells, whereas growth-arresting genes, such as the transforming growth factor beta1 (TGF-beta1) gene, extracellular matrix (ECM)-related genes, and cyclin-dependent kinase inhibitor genes, were up-regulated. Ahr-/- fibroblasts secreted significantly more TGF-beta1 into the culture medium than Ahr+/+ fibroblasts did, and Ahr-/- showed increased levels of activated Smad4 and TGF-beta1 mRNA. Inhibition of TGF-beta1 signaling by overexpression of Smad7 reversed the proliferative and gene expression phenotype of Ahr-/- fibroblasts. Changes in TGF-beta1 mRNA accumulation were due to stabilization resulting from decreased activity of TTP, the tristetraprolin RNA-binding protein responsible for mRNA destabilization through AU-rich motifs. These results show that the Ah receptor possesses interconnected intrinsic cellular functions, such as ECM formation, cell cycle control, and TGF-beta1 regulation, that are independent of activation by either exogenous or endogenous ligands and that may play a crucial role during tumorigenesis.